sweep: match.red_card_shown (bool)
β lab Β· AUC 0.609 (real signal) Β· ran 7/5/2026
What this is: Asks which pre-match factors drive one specific outcome, using a walk-forward model and permutation importance.
| Factor | Importance | Direction | Survives all eras? |
|---|
| home__t3__team.elo | 0.0143 | β -0.185 | no |
| away__team.congestion_21d | 0.0000 | β +0.032 | no |
| away__team.elo_momentum_l5 | 0.0000 | β | no |
| away__team.elo | 0.0000 | β -0.061 | no |
| away__team.goal_diff_avg_l5 | 0.0000 | β | no |
| away__team.goals_against_avg_l5 | 0.0000 | β | no |
| away__team.goals_for_avg_l5 | 0.0000 | β | no |
| away__team.form_points_l5 | 0.0000 | β | no |
| away__team.matches_since_clean_sheet | 0.0000 | β -0.093 | no |
| away__team.matches_since_win | 0.0000 | β -0.041 | no |
| away__team.pass_acc_avg_l5 | 0.0000 | β +0.073 | no |
| away__team.possession_avg_l5 | 0.0000 | β +0.050 | no |
Reading the columnswhat each number actually means
| AUC | predictability: 0.50 = coin flip, ~0.70 = ceiling for sports |
| Importance | how much the model leans on this factor (permutation importance) |
| Direction | sign of the raw correlation with the outcome |
| Survives all eras | effect points the same way in every historical era |
Spec Β· the reproducible recipe
{
"name": "sweep: match.red_card_shown (bool)",
"sport": "football",
"target": {
"metric": "match.red_card_shown"
},
"features": "all"
}